Yanyan Wang

Bio: Yanyan Wang is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Least mean squares filter & Norm (mathematics). The author has an hindex of 12, co-authored 38 publications receiving 604 citations.

Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation

Abstract: In this paper, we propose a type of sparsity-aware set-membership normalized least mean square (SM-NLMS) algorithm for sparse channel estimation and echo cancelation. The proposed algorithm incorporates an l 1 -norm penalty into the cost function of the conventional SM-NLMS algorithm to exploit the sparsity of the sparse systems, which is denoted as zero-attracting SM-NLMS (ZASM-NLMS) algorithm. Furthermore, an improved ZASM-NLMS algorithm is also derived by using a log-sum function instead of the l 1 -norm penalty in the ZASM-NLMS, which is denoted as reweighted ZASM-NLMS (RZASM-NLMS) algorithm. These zero-attracting SM-NLMS algorithms are equivalent to adding shrinkages in their update equations, which result in fast convergence speed and low estimation error when most of the unknown channel coefficients are zero or close to zero. These proposed algorithms are described and analyzed in detail, while the performances of these algorithms are investigated by using computer simulations. The simulation results obtained from sparse channel estimation and echo cancelation demonstrate that the proposed sparse SM-NLMS algorithms are superior to the previously proposed NLMS, SM-NLMS as well as zero-attracting NLMS (ZA-NLMS) algorithms.

A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

Abstract: A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance.

Noise-Free Maximum Correntropy Criterion Algorithm in Non-Gaussian Environment

Abstract: In this brief, a noise-free maximum correntropy criterion (NFMCC) algorithm is proposed for system identification in non-Gaussian environments. The proposed algorithm utilizes correntropy theory to construct a cost function which is realized based on a normalized Gaussian kernel. In addition, a new dynamic step size scheme is proposed to enhance the performance of the proposed algorithm, which is implemented by minimizing the noise-free a posteriori error signal, and the mean square deviation (MSD) is greatly decreased. The proposed NFMCC algorithm shows significant property in reducing the detrimental effects of outliers and impulsive noise with different input signals. Moreover, a Students’ T distributed noise is employed to evaluate the effectiveness of the proposed algorithm in terms of the MSD and convergence for heavy tailed noising environment. The parameter effects on the NFMCC algorithm are also presented, and its performance is investigated on a real-life channel that is measured in underwater. Simulation results prove the effectiveness of the proposed algorithm which provides a considerable computational complexity and an acceptable running time.

Multi-band metamaterial-based microstrip antenna for WLAN and WiMAX applications

Abstract: Two multi-band metamaterial based microstrip antennas are designed for WLAN and WiMAX applications. Antenna 1 consists of a simple rectangle monopole radiator with a 2×3 complementary split ring resonator (CSRR) printed on its back ground. By using CSRR technique, two frequency bands operating at 3.2–3.9 GHz and 5.65–5.8 GHz are achieved. Antenna 2 is composed of a rectangle patch with a CSRR slot and a conventional ground plane, which can provide a triple-band characteristic covering 2.4–2.48 GHz, 3.3–3.9 GHz and 5.15–5.7 GHz. The operational bands of these two antennas can be used for WiMAX at 2.5/3.5/5.5 GHz and WLAN at 2.4/5.2/5.8 GHz, and it has omnidirectional radiation patterns, which can be the good candidate for WiMAX and WLAN applications.

Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications

Abstract: A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. Simulation results indicate that the HNC-PNMCC is better than the PNMCC, MCC, and sparse MCC with respect to the estimation performance for the cluster-sparse system identification under the impulsive noises.

A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

Abstract: A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance.

Mixed Norm Constrained Sparse APA Algorithm for Satellite and Network Echo Channel Estimation

Abstract: We propose an improved proportionate affine projection algorithm (PAPA), which is realized by integrating a hybrid-norm constraint into the affine projection algorithm (APA) to estimate the cluster sparse signals that are happened in satellite and network echo channels. The proposed algorithm optimizes the hybrid ${l_{2,0}}$ -norm of the filter coefficients, and the PAPA is a special case of the proposed method. Moreover, an enhanced PAPA is modified for the cluster sparse channel estimations. Various simulation experiments are performed to verify that the proposed algorithms are superior to the APA, PAPA, and related sparse algorithms with different input signals and various parameters.

Noise-Free Maximum Correntropy Criterion Algorithm in Non-Gaussian Environment

Abstract: In this brief, a noise-free maximum correntropy criterion (NFMCC) algorithm is proposed for system identification in non-Gaussian environments. The proposed algorithm utilizes correntropy theory to construct a cost function which is realized based on a normalized Gaussian kernel. In addition, a new dynamic step size scheme is proposed to enhance the performance of the proposed algorithm, which is implemented by minimizing the noise-free a posteriori error signal, and the mean square deviation (MSD) is greatly decreased. The proposed NFMCC algorithm shows significant property in reducing the detrimental effects of outliers and impulsive noise with different input signals. Moreover, a Students’ T distributed noise is employed to evaluate the effectiveness of the proposed algorithm in terms of the MSD and convergence for heavy tailed noising environment. The parameter effects on the NFMCC algorithm are also presented, and its performance is investigated on a real-life channel that is measured in underwater. Simulation results prove the effectiveness of the proposed algorithm which provides a considerable computational complexity and an acceptable running time.

Enhanced OFDM-NOMA for next generation wireless communication: A study of PAPR reduction and sensitivity to CFO and estimation errors

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) based on Non-Orthogonal Multiple Access (NOMA) has been previously studied to fulfil the demands of high spectral efficiency, massive connectivity and resilience to frequency selectivity for the upcoming fifth generation (5G) wireless communication and beyond. NOMA enables spectrum overlapping and allows distinct users to simultaneously operate over the same frequency band, and thus enables massive connectivity. High Peak-to-Average Power Ratio (PAPR) and sensitivity to Carrier Frequency Offset (CFO) are significant demerits to deploy such a multicarrier system for 5G and beyond applications. This paper studies the problem of high PAPR and the presence of CFO with efficient pre-coding techniques and a very simplified receiver design. An improved Minimum Mean Square Error (MMSE) receiver is proposed for low-complexity Joint Equalization and CFO Compensation (JECC) in frequency domain using Banded-Matrix Implementation (BMI). Moreover, we have investigated the sensitivity of different pre-coding techniques to channel and CFO estimation errors.